The influence of microstructure and composition on the plastic behaviour of dual-phase steels

Corrosion-resistant high entropy alloy with high strength and ductility

Neural networks are now a prominent feature of materials science with rapid progress in all sectors of the subject. It is premature, however, to claim that the method is established. There are genuine difficulties caused by the often incomplete exploration and publication of models. The assessment presented here is an attempt to compile a loose set of guidelines for maximising the impact of any models that are created, in order to encourage thoroughness in publication to a point where the work can be independently verified.

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Performance of neural networks in materials science

Metal additive manufacturing (MAM) is an emerging technology to produce complex end-use metallic parts. To adopt MAM for manufacturing numerous engineering parts used in critical applications, a thorough understanding of the relationship between the complex thermal cycles in MAM and the unique heterogeneous microstructures of MAM parts need to be established. This review article provides a comprehensive overview of the evolution of heterogeneous microstructures in MAM parts, including melt pool boundaries, heterogeneous grain structure, sub-grain cellular structure, matrix supersaturation, segregation, phase transformation, oxides formation, and texture. The evolution of residual stresses and the anisotropy in MAM parts and the post-MAM heat treatment effects on the microstructural evolution are also discussed. This review covers the microstructural aspects of most engineering materials in particular steels, high entropy alloys, aluminum alloys, titanium alloys, nickel-base superalloys, and copper alloys, with a primary focus on the parts manufactured using selective laser melting, direct energy deposition, and electron beam melting processes.

Heterogeneous Aspects of Additive Manufactured Metallic Parts: A Review

Many studies monitoring the formation of martensite during the tensile deformation of austenite report data which are, in principle, affected by both the applied stress and the resulting plastic strain. It is not clear in these circumstances whether the transformation is stress induced (i.e. the stress provides a mechanical driving force) or whether the generation of defects during deformation helps nucleate martensite in a scenario better described as strain induced transformation. The authors demonstrate in the present work that a large amount of published data relating the fraction of martensite to plastic strain can in fact be described in terms of the pure thermodynamic effect of applied stress.

Analysis of deformation induced martensitic transformation in stainless steels

Prediction of hole expansion ratio for automotive grade steels

Effect of build geometry and orientation on microstructure and properties of additively manufactured 316L stainless steel by laser metal deposition

The effects of strain rate and temperature on the strain induced transformation behaviour of retained austenite and hence mechanical properties of TRIP-aided steels with annealed martensite (AM), bainitic ferrite (BF) and polygonal ferrite (PF) matrix microstructures were examined. For this, tensile tests were carried out at four different strain rates varying from 3.33×10−5 s−1 to 3.33×10−2 s−1 at room temperature and at 150°C. Additional intermittent tests were carried out at intermediate strain rates to estimate the extent of strain induced transformation of austenite to martensite using X-ray diffraction analysis. Tensile strength (TS) decreased with strain rate at both temperatures, though the TS at 150°C was lower for all steels except for BF steel tested at 3.33×10−5 s−1. The total elongation (TEL) for PF steel significantly improved at 150°C at all strain rates, with a maximum of 78% obtained at 3.33×10−3 s−1, the corresponding TS being 930 MPa. A similar effect was seen in AM steels tested at 3.33×10−3 s−1 and 3.33×10−4 s−1. However, in BF steel, higher temperature had a detrimental effect on TEL, especially at strain rates of 3.33×10−2 s−1 and 3.33×10−3 s−1.

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Strain-induced Transformation Behaviour of Retained Austenite and Tensile Properties of TRIP-aided Steels with Different Matrix Microstructure

Experimental data published in literature were used to develop a neural network model to predict the strain induced transformation behaviour of retained austenite as a function of 13 input variables including chemical composition of the steel, initial retained austenite content, matrix microstructure and forming conditions. The model was found to make reasonable predictions with respect to established metallurgical principles and other published data.

Monideepa Mukherjee

Papers

Prediction of hole expansion ratio for automotive grade steels

Effect of build geometry and orientation on microstructure and properties of additively manufactured 316L stainless steel by laser metal deposition

Strain-induced Transformation Behaviour of Retained Austenite and Tensile Properties of TRIP-aided Steels with Different Matrix Microstructure

Neural network analysis of strain induced transformation behaviour of retained austenite in TRIP-aided steels

Determination of bulk flow properties of a material from the flow properties of its constituent phases